{"id":8366,"date":"2026-08-10T09:55:06","date_gmt":"2026-08-10T09:55:06","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-programming-generative-ai-2024-10\/"},"modified":"2026-08-10T09:55:06","modified_gmt":"2026-08-10T09:55:06","slug":"oreilly-programming-generative-ai-2024-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-programming-generative-ai-2024-10\/","title":{"rendered":"Oreilly \u2013 Programming Generative AI 2024-10"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"7:1-7:577\"><span style=\"vertical-align: inherit\">Programming Generative AI Course. This hands-on course takes you from building simple neural networks in PyTorch to working with large multi-faceted models capable of understanding text and images simultaneously. Along the way, you\u2019ll learn how to train your own generative models from scratch to generate infinite images, generate text using large language models like ChatGPT, write your own text-to-image pipeline to understand how notification-based generative models work, and customize large pre-trained models like Persistent Publishing to generate images of new topics with unique visual styles (and more).<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"9:1-9:25\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"10:1-21:0\">\n<li data-sourcepos=\"10:1-10:83\"><span style=\"vertical-align: inherit\">Training a variational AutoCAD with PyTorch to learn a compact latent space from images<\/span><\/li>\n<li data-sourcepos=\"11:1-11:80\"><span style=\"vertical-align: inherit\">Generate and edit realistic human faces with unconditional diffusion models and SDEdit<\/span><\/li>\n<li data-sourcepos=\"12:1-12:82\"><span style=\"vertical-align: inherit\">Using large language models like GPT2 to generate text with Hugging Face Transformers<\/span><\/li>\n<li data-sourcepos=\"13:1-13:82\"><span style=\"vertical-align: inherit\">Performing text-based semantic image search using multi-faceted models such as CLIP<\/span><\/li>\n<li data-sourcepos=\"14:1-14:107\"><span style=\"vertical-align: inherit\">Programming your own text-to-image pipeline to understand how notification-based generative models like persistent publishing work<\/span><\/li>\n<li data-sourcepos=\"15:1-15:52\"><span style=\"vertical-align: inherit\">Properly evaluating generative models, both qualitatively and quantitatively<\/span><\/li>\n<li data-sourcepos=\"16:1-16:66\"><span style=\"vertical-align: inherit\">Automatic image description using pre-trained base models<\/span><\/li>\n<li data-sourcepos=\"17:1-17:76\"><span style=\"vertical-align: inherit\">Producing images in a specific visual style with efficient fine-tuning of stable propagation with LoRA<\/span><\/li>\n<li data-sourcepos=\"18:1-18:114\"><span style=\"vertical-align: inherit\">Create personalized AI avatars by teaching new topics and concepts to pre-trained publishing models with Dreambooth<\/span><\/li>\n<li data-sourcepos=\"19:1-19:87\"><span style=\"vertical-align: inherit\">Guiding the structure and composition of generated images using depth- and edge-conditioned ControlNets<\/span><\/li>\n<li data-sourcepos=\"20:1-21:0\"><span style=\"vertical-align: inherit\">Perform near-real-time inference with SDXL Turbo for frame-by-frame video-to-video translation<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"22:1-22:31\"><span style=\"vertical-align: inherit\">This course is suitable for people who:<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"23:1-28:0\">\n<li data-sourcepos=\"23:1-23:91\"><span style=\"vertical-align: inherit\">Engineers and developers interested in building productive AI systems and applications<\/span><\/li>\n<li data-sourcepos=\"24:1-24:65\"><span style=\"vertical-align: inherit\">Data scientists interested in working with advanced deep learning models<\/span><\/li>\n<li data-sourcepos=\"25:1-25:100\"><span style=\"vertical-align: inherit\">Students, researchers, and academics looking for a practical or applied resource to supplement their theoretical or conceptual knowledge<\/span><\/li>\n<li data-sourcepos=\"26:1-26:72\"><span style=\"vertical-align: inherit\">Technical artists and creative coders who want to enhance their creative practice<\/span><\/li>\n<li data-sourcepos=\"27:1-28:0\"><span style=\"vertical-align: inherit\">Anyone who is interested in working with productive AI and doesn\u2019t know where or how to start<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Generative AI Programming Course Specifications<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/programming-generative-ai\/9780135381090\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/jonathan-dinu\/\"><span style=\"vertical-align: inherit\">Jonathan Dinu<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 9 hours and 24 minutes<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<div class=\"ud-block-list-item ud-block-list-item-small ud-block-list-item-tight ud-block-list-item-neutral ud-text-sm\" dir=\"ltr\" style=\"text-align: left\">\n<ul>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Introduction<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Programming Generative AI: Introduction<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Lesson 1: The What, Why, and How of Generative AI<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Topics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.1 Generative AI in the Wild<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.2 Defining Generative AI<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.3 Multitudes of Media<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.4 How Machines Create<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.5 Formalizing Generative Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.6 Generative versus Discriminative Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.7 The Generative Modeling Trilemma<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">1.8 Introduction to Google Collab<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Lesson 2: PyTorch for the Impatient<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Topics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.1 What is PyTorch?<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.2 The PyTorch Layer Cake<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.3 The Deep Learning Software Trilemma<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.4 What Are Tensors, Really?<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.5 Tensors in PyTorch<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.6 Introduction to Computational Graphs<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.7 Backpropagation Is Just the Chain Rule<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.8 Effortless Backpropagation with torch.autograd<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.9 PyTorch\u2019s Device Abstraction (ie, GPUs)<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.10 Working with Devices<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.11 Components of a Learning Algorithm<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.12 Introduction to Gradient Descent<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.13 Getting to Stochastic Gradient Descent (SGD)<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.14 Comparing Gradient Descent and SGD<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.15 Linear Regression with PyTorch<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.16 Perceptrons and Neurons<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.17 Layers and Activations with torch.nn<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.18 Multi-layer Feedforward Neural Networks (MLP)<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Lesson 3: Latent Space Rules Everything Around Me<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Topics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.1 Representing Images as Tensors<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.2 Desiderata for Computer Vision<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.3 Features of Convolutional Neural Networks<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.4 Working with Images in Python<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.5 The FashionMNIST Dataset<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.6 Convolutional Neural Networks in PyTorch<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.7 Components of a Latent Variable Model (LVM)<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.8 The Humble Autoencoder<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.9 Defining an Autoencoder with PyTorch<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.10 Setting up a Training Loop<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.11 Inference with an Autoencoder<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.12 Look Ma, No Features!<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.13 Adding Probability to Autoencoders (VAE)<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.14 Variational Inference: Not Just for Autoencoders<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.15 Transforming an Autoencoder into a VAE<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.16 Training a VAE with PyTorch<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.17 Exploring Latent Space<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.18 Latent Space Interpolation and Attribute Vectors<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Lesson 4: Demystifying Diffusion<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Topics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.1 Generation as a Reversible Process<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.2 Sampling as Iterative Denoising<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.3 Diffusers and the Hugging Face Ecosystem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.4 Generating Images with Diffuser Pipelines<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.5 Deconstructing the Diffusion Process<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.6 Forward Process as Encoder<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.7 Reverse Process as Decoder<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.8 Interpolating Diffusion Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.9 Image-to-Image Translation with SDEdit<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">4.10 Image Restoration and Enhancement<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Lesson 5: Generating and Encoding Text with Transformers<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Topics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.1 The Natural Language Processing Pipeline<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.2 Generative Models of Language<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.3 Generating Text with Transformers Pipelines<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.4 Deconstructing Transformer Pipelines<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.5 Decoding Strategies<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.6 Transformers are Just Latent Variable Models for Sequences<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.7 Visualizing and Understanding Attention<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.8 Turning Words into Vectors<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.9 The Vector Space Model<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.10 Embedding Sequences with Transformers<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.11 Computing the Similarity Between Embeddings<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.12 Semantic Search with Embeddings<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">5.13 Contrastive Embeddings with Sentence Transformers<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Lesson 6: Connecting Text and Images<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Topics <\/span><br \/><span style=\"vertical-align: inherit\">6.1 Components of a Multimodal Model<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.2 Vision-Language Understanding<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.3 Contrastive Language-Image Pretraining<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.4 Embedding Text and Images with CLIP<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.5 Zero-Shot Image Classification with CLIP<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.6 Semantic Image Search with CLIP<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.7 Conditional Generative Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.8 Introduction to Latent Diffusion Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.9 The Latent Diffusion Model Architecture<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.10 Failure Modes and Additional Tools<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.11 Stable Diffusion Deconstructed<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.12 Writing Our Own Stable Diffusion Pipeline<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.13 Decoding Images from the Stable Diffusion Latent Space<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.14 Improving Generation with Guidance<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">6.15 Playing with Prompts<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Lesson 7: Post-Training Procedures for Diffusion Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Topics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.1 Methods and Metrics for Evaluating Generative AI<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.2 Manual Evaluation of Stable Diffusion with DrawBench<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.4 Overview of Methods for Fine-Tuning Diffusion Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.5 Sourcing and Preparing Image Datasets for Fine-Tuning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.6 Generating Automatic Captions with BLIP-2<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.7 Parameter Efficient Fine-Tuning with LoRA<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.8 Inspecting the results of fine-tuning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.9 Inference with LoRAs for Style-Specific Generation<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.10 Conceptual Overview of Textual Inversion<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.11 Subject-Specific Personalization with Dreambooth<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.12 Dreambooth versus LoRA Fine-Tuning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.13 Dreambooth Fine-Tuning with Hugging Face<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.14 Inference with Dreambooth to Create Personalized AI Avatars<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.15 Adding Conditional Control to Text-to-Image Diffusion Models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.16 Creating Edge and Depth Maps for Conditioning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.17 Depth and Edge-Guided Stable Diffusion with ControlNet<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.18 Understanding and Experimenting with ControlNet Parameters<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.19 Generative Text Effects with Font Depth Maps<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.20 Few Step Generation with Adversarial Diffusion Distillation (ADD)<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.21 Reasons to Distill<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.22 Comparing SDXL and SDXL Turbo<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.23 Text-Guided Image-to-Image Translation<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.24 Video-Driven Frame-by-Frame Generation with SDXL Turbo<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">7.25 Near Real-Time Inference with PyTorch Performance Optimizations<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Programming Generative AI: Summary<\/span><\/li>\n<\/ul>\n<\/div>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course prerequisites<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Comfortable programming in Python<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Knowledge of machine learning basics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Familiarity with deep learning and neural networks will be helpful but is not required<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-958669 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/01\/Programming-Generative-AI.png\" alt=\"Generative AI Programming\" width=\"1238\" height=\"432\"><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Sample course video<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-153181-1\"><video class=\"wp-video-shortcode\" id=\"video-153181-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Programming_Generative_AI_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Programming_Generative_AI_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Programming_Generative_AI_Downloadly.ir.mp4?nocache=1786112098141\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Programming_Generative_AI_Downloadly.ir.mp4<\/a><\/video><\/mediaelementwrapper><\/div>\n<div class=\"mejs-layers\">\n<div class=\"mejs-poster mejs-layer\" style=\"display: none; width: 100%; height: 100%;\"><\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-loading\"><span class=\"mejs-overlay-loading-bg-img\"><\/span><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-error\"><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer mejs-overlay-play\" style=\"width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-button\" role=\"button\" tabindex=\"0\" aria-label=\"Play\" aria-pressed=\"false\"><\/div>\n<\/div>\n<\/div>\n<div class=\"mejs-controls\">\n<div class=\"mejs-button mejs-playpause-button mejs-play\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Play\" aria-label=\"Play\" tabindex=\"0\"><\/button><\/div>\n<div class=\"mejs-time mejs-currenttime-container\" role=\"timer\" aria-live=\"off\"><span class=\"mejs-currenttime\">00:00<\/span><\/div>\n<div class=\"mejs-time-rail\"><span class=\"mejs-time-total mejs-time-slider\" role=\"slider\" tabindex=\"0\" aria-label=\"Time Slider\" aria-valuemin=\"0\" aria-valuemax=\"0\" aria-valuenow=\"0\" aria-valuetext=\"00:00\"><span class=\"mejs-time-buffering\" style=\"display: none;\"><\/span><span class=\"mejs-time-loaded\"><\/span><span class=\"mejs-time-current\"><\/span><span class=\"mejs-time-hovered no-hover\"><\/span><span class=\"mejs-time-handle\"><span class=\"mejs-time-handle-content\"><\/span><\/span><span class=\"mejs-time-float\"><span class=\"mejs-time-float-current\">00:00<\/span><span class=\"mejs-time-float-corner\"><\/span><\/span><\/span><\/div>\n<div class=\"mejs-time mejs-duration-container\"><span class=\"mejs-duration\">00:00<\/span><\/div>\n<div class=\"mejs-button mejs-volume-button mejs-mute\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Mute\" aria-label=\"Mute\" tabindex=\"0\"><\/button><a href=\"javascript:void(0);\" class=\"mejs-volume-slider\" aria-label=\"Volume Slider\" aria-valuemin=\"0\" aria-valuemax=\"100\" role=\"slider\" aria-orientation=\"vertical\"><span class=\"mejs-offscreen\">Use Up\/Down Arrow keys to increase or decrease volume.<\/span><\/p>\n<div class=\"mejs-volume-total\">\n<div class=\"mejs-volume-current\" style=\"bottom: 0px; height: 100%;\"><\/div>\n<div class=\"mejs-volume-handle\" style=\"bottom: 100%; margin-bottom: -3px;\"><\/div>\n<\/div>\n<p><\/a><\/div>\n<div class=\"mejs-button mejs-fullscreen-button\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Fullscreen\" aria-label=\"Fullscreen\" tabindex=\"0\"><\/button><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div dir=\"ltr\" style=\"text-align: left\">\n<h3><span style=\"vertical-align: inherit\">Installation Guide<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">After Extract, view with your favorite player.<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Subtitles: None<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Quality: 720p<\/span><\/p>\n<\/div>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Download link<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Programming_Generative_AI_2024-10.part1_Downloadly.ir.rar?nocache=1786112097\"><span style=\"vertical-align: inherit\">Download Part 1 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Programming_Generative_AI_2024-10.part2_Downloadly.ir.rar?nocache=1786112097\"><span style=\"vertical-align: inherit\">Download Part 2 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Programming_Generative_AI_2024-10.part3_Downloadly.ir.rar?nocache=1786112097\"><span style=\"vertical-align: inherit\">Download Part 3 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Programming_Generative_AI_2024-10.part4_Downloadly.ir.rar?nocache=1786112097\"><span style=\"vertical-align: inherit\">Download Part 4 \u2013 887 MB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File size<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.8 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Programming Generative AI Course. This hands-on course takes you from building simple neural networks in PyTorch to working with large multi-faceted<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[74178,74179,74180,74181,74182,74183],"class_list":["post-8366","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-course-programming-generative-ai","dgi_tag-download-course-programming-generative-ai","dgi_tag-download-programming-generative-ai","dgi_tag-free-download-programming-generative-ai","dgi_tag-free-programming-generative-ai","dgi_tag-jonathan-dinu"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8366","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item"}],"about":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/types\/digital_item"}],"author":[{"embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8366\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=8366"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=8366"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=8366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}